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Found 3 papers, 1 papers with code
Dynamic Shuffle: An Efficient Channel Mixture Method
To reduce the data-dependent redundancy, we devise a dynamic shuffle module to generate data-dependent permutation matrices for shuffling.
On the Robustness of Open-World Test-Time Training: Self-Training with Dynamic Prototype Expansion
Existing approaches often focus on improving test-time training performance under well-curated target domain data.
A Bayesian Lasso based Sparse Learning Model
This paper develops a new sparse learning model, named the Bayesian Lasso Sparse (BLS) model, that takes the hierarchical model formulation of the Bayesian Lasso.
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